Aug 27, 2017
Quite challenging but also quite a sense of accomplishment when you finish the course. I learned a lot and think this was the course I preferred of the entire specialization. I highly recommend it!
May 04, 2019
Lectures are very good with a perfect explanation. More than lectures I liked the assignment questions. They are worth doing. You will get to know the basic foundation of text mining. :-)
By Mark M•
Aug 20, 2017
This is the 4th one and also a very important building block in the data science specialization. However comparing to the other courses there is much talk from the lecturer and not so much of interesting background information of this topic. So this course does not go far beyond a good tutorial.
By Reed R•
Mar 07, 2018
Overall a good course and a nice introduction to Text Mining but issues with the autograder and some unclear instructions can make the assignments a little painful.
By Yulo L•
Jan 16, 2018
The course Assignments could be more clear and consistent with what is actually taught in the class. A good example is when n-grams were required to calculate the similarities, but have actually not been introduced in the video yet.
Also, an expected answers would be nice for the assignments.
Other than that, it was a nice introduction to NLP in Python.
By Girija M•
Jul 01, 2018
The subject is too vast to be covered in such small videos. Lot more details can be integrated. Great start for beginners to Text Mining though
By Qian H•
Oct 04, 2017
The homework is quite not related to the lecture. And it is so hard to finish.
Apr 24, 2018
Assignments were too difficult.
By Mile D•
Nov 23, 2017
This course was quit ok. I have expected just more exercises and explanations because of the difficult topic.
By James M•
Apr 18, 2018
Autograder bugs make for a frustrating time completing the assignments. Independent research and self-guided learning will come in handy for this course as the lectures (mostly) are uninformative.
By Siwei Y•
Sep 14, 2017
autograder 经常 犯些低级错误， 导致很多人在对付 autograder 上 花了很多时间。 请授课方务必改正， 否则 有不负责任之嫌。另外 编程作业的 说明委实不清不楚， 模棱两可。除此之外内容还算中规中矩， 虽然我个人 认为太表浅了一些。
Autograder is so buggy, that people have to spend lots of time to figure out, what the solution is.
Additionally, the Instuction of python assignment is often ambiguous. Please fix them ASAP.
Personally I find that the content is somehow like an introduction. I had hoped something more about detail.
By Maxime R•
Mar 13, 2018
I really think that the 3rd and 4th week of the course should have more practical presentation (especially the 4th week for which the assignment is quite 'new' in terms of programming). Having a notebook for the 4th week would be a good additional material.
By Jim B•
Aug 24, 2017
Of all of the Applied Data Science with Python classes I have taken, this was the worst. If it were not for the discussion groups I would not have been able to complete the course. And the discussions groups requested help from instructors and received little to none. Part of the problem is that the auto-graders were broken, the rest of the problem was that this class relied on the online documentation. And of the classes in Applied Data Science with Python, this one has the worst documentation. Hence the class needed more help.
By Ashwini B•
Jun 03, 2018
Topics like LDA need better explanations.
By Sara C•
May 17, 2018
I like the lecturer.
By Thomas B•
Apr 22, 2018
Some rather vague assignments instructions, some assignments require material only briefly mentioned in lectures
By Steve M•
May 03, 2018
The content of this course has great potential, but needs significant refinement. The lectures, while delivered with enthusiasm, were very theoretical/academic and provided little in the way of preparation for the more practical exercises. The disconnect between lectures and assignments, coupled with technical challenges (autograder glitches) were frustrating. The only support came from one dedicated volunteer Coursera Mentor; the instructor cadre was absent or unavailable to students throughout the four week period. The topics of text mining and Natural Language Processing are central to data science, and deserve better instruction than this course delivered.
By Max P•
Jan 06, 2018
Although the topic of Text Mining is very interesting, I find that the AP did not dive deep enough into the various topics. The matter that he did explain was interesting, but at some parts not really clear. I missed a clear line of thought.
Concerning the assignments: very interesting topics, but the guidelines could be clarified to nip any possible confusion in the bud. Also, some exercises could be split up into multiple ones so that debugging becomes easy. Many students in the Discussion Forum mentioned difficulties.
By Ben E•
Nov 10, 2017
This course did cover some good topics (Naive Bayes model, similarity, part of speech tagging). However, I felt the homework was more about manipulating Python data structures than learning anything significant about text mining. Some of the theory behind the models was covered, but didn't make it to the homework.
It would be difficult since this is a short class, but I would have preferred more about tips on which model to use and feature engineering / selection, and examples of practical applications of text mining. (Or stories of failures in the instructors' experience!)
By Vijay C S•
Oct 24, 2017
The course is pitched at a introductory level. I would have like to have more practical tutorials.
By Siddharth S•
Jun 12, 2018
The fact that the strategy of a Jupyter Notebook Demonstration during explanation was not followed in week 3 and 4 was a disappointment.This specialisation had been wonderful with its use of demonstration in Lectures with the Notebook,If this had been followed in Week 3 and Week 4 then the course would definitely had shined.Please correct the same, the course deserves that, It has wonderful content.
By Panit A•
Oct 22, 2017
Bad assignment. Grader not reliable. No control over the discussion board, many confusing comments mixed with good comments.
By Nitish K•
Sep 16, 2017
While the course gives a good broad understanding of how any NLP task would work in theory, but the course is very unstructured. For example, if I had to be a given task on doing a sentiment analysis, I can broadly tell what is the conceptual theory behind it but I dont know how exactly to do it because the professor talked about so many tools which were repetitive in their use and were not clearly demarcated as to what tool should be used for what?
By Paula C R•
Aug 04, 2017
I think the course was superficial and could be better explored. It's good start, though.
By Brian R v K•
Oct 30, 2017
I enjoyed this course, but some aspects of it felt "light touch", particularly week 4. That week would be greatly improved with a jupyter notebook and an applied demonstration by the absolutely awesome Teaching Assistant, Filip Jankovic. Whenever he does a demonstration, it's clear, concise, practical, and always helpful. Let's see more of him!
By Jonathan B•
Aug 10, 2017
While the video of the course were OK, the assignments were of really bad quality. So many problems with the auto-grader, and some questions were absolutely not clear. I still put 3 stars because the subject is interesting and I got things I can work with out of it, but don't expect too much from it, you'll spend most of your time trying to deal with the weird assignments questions. For the time spent, they could have added 1 or 2 weeks of videos.
By samuel e•
Oct 01, 2017
The grading system is supremely messed up and at least I have a vague idea what am talking about because I have completed more than a dozen coursera courses. Also, the methods used through the courses teaches very bad coding approach relying on global variables.
Below is an example from Module 2:
return len(set(nltk.word_tokenize(moby_raw))) # or alternatively len(set(text1))
Why would they not pass moby_raw and text1 as arguments in the function?
With that said, the course could easily be one of the best intro NLTK courses out there minus the frustration and very poor design.